python-logreduce
Log file anomaly extractor
Based on success logs, logreduce highlights useful text in failed logs. The goal is to save time in finding a failure's root cause. On average, learning run at 2000 lines per second, and testing run at 1300 lines per seconds. logreduce uses a *model* to learn successful logs and detect novelties in failed logs: * Random words are manually removed using regular expression * Then lines are converted to a matrix of token occurrences (using **HashingVectorizer**), * An unsupervised learner implements neighbor searches (using **NearestNeighbors**).
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